Sentiment Analysis with Comparison Enhanced Deep Neural Network

Abstract

Sentiment analysis is a significant task in Natural Language Processing. It refers to classification based on the emotional tendency in text by extracting text features. The existing results show that models based on RNN and CNN have good performance. In order to improve the performance of text sentiment analysis, we reformulate the classification task as a comparing problem, and propose Comparison Enhanced Bi-LSTM with Multi-Head Attention (CE-B-MHA). In fact, it is efficient to classify by comparison mechanism instead of doing complex calculation. In this model, bidirectional LSTM is used for initial feature extraction, and valuable information is extracted from different dimensions and representation subspaces by Multi-Head Attention. The comparison mechanism aims to score the feature vectors by comparing with the labeled vectors. The experimental results show that CE-B-MHA has better performance than many existing models on three sentiment analysis datasets.